AI-Driven Intra-Tenant Resource Threshold Adjustment
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Solution Overview
Problem
Current cloud computing systems lack the ability to set intra-tenant resource limits and utilize artificial intelligence or machine learning to predict future resource usage and optimize resource allocation, leading to inefficiencies and resource waste.
Innovation Solution
Implementing a system that uses AI and ML engines to monitor and predict future resource usage, adjust intra-tenant resource thresholds, and generate alerts for optimal resource allocation, thereby optimizing resource utilization and reducing waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud providers allocate resources to applications without AI-based prediction and monitoring, then resource allocation is simple and fast, but resource utilization efficiency is low and waste occurs
Solution Approach 1:
The system performs preliminary actions by predicting future resource usage patterns using AI/ML models before actual resource allocation decisions are made. The machine learning models analyze historical data and forecast future needs, allowing the system to proactively adjust resource thresholds and allocations rather than reacting to current usage alone.
Solution Approach 2:
The system implements continuous feedback loops where resource usage is monitored, AI models predict future usage based on current patterns, and intra-tenant thresholds are automatically adjusted. This closed-loop feedback mechanism enables the system to learn from actual usage patterns and optimize resource allocation dynamically, improving utilization efficiency while managing complexity through automated decision-making.
2Loss of energy
If intra-tenant resource limits are not enforced, then applications have unlimited resource access and can scale freely, but resource waste and cost increase occur
Solution Approach 1:
The system dynamically adjusts intra-tenant resource thresholds based on predicted future usage patterns rather than using static limits. The AI models continuously refine predictions based on actual usage data, and the resource thresholds are automatically updated to match evolving application needs. This dynamic approach prevents resource waste while maintaining the ability for applications to scale as their needs change.
Solution Approach 2:
The system changes the parameter of resource allocation thresholds based on predicted usage patterns. By using machine learning to forecast future resource needs, the system adjusts the intra-tenant limits from fixed values to adaptive parameters that reflect actual application requirements. This allows the system to optimize resource utilization while preserving application scalability through data-driven parameter adjustment.
3Reliability
If resource allocation is based only on current usage without future prediction, then the system is simple to operate, but future resource needs may not be met or resources are over-allocated
Solution Approach 1:
The system performs self-service by automatically using AI/ML models to predict future resource usage and adjust intra-tenant thresholds without requiring manual intervention. The machine learning models autonomously analyze historical data, forecast future needs, and optimize resource allocation decisions. This automated self-service approach maintains operational simplicity while significantly improving reliability through intelligent prediction and automatic adaptation to changing requirements.
Data Source
AI summary
A method includes receiving a reservation request corresponding to resource requirements of an application. The reservation request including an amount of resources requested for the application. Determining an initial intra-tenant threshold based on the reservation request. Reserving an amount of intra-tenant resources. The amount of intra-tenant resources reserved being greater than the amount of resources requested. Monitoring tenant resource usage assigned to execute the application. The method further includes storing resource usage data periodically. The method further includes predicting future tenant resource usage based on the resource usage data. The method further includes responsive to the predicted future tenant resource usage, performing at least one of: determining a new intra-tenant threshold to be recommended in response to the initial intra-tenant threshold being set too high or too low, or generating an alert indicating that the initial intra-tenant threshold is insufficient to support the predicted future tenant resource usage.